EURASIP Journal on Applied Signal Processing
Volume 2006 (2006), Article ID 17021, 9 pages
doi:10.1155/ASP/2006/17021
Particle Filter Design Using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environments
1Western Australian Telecommunications Research Institute, 35 Stirling Highway, Crawley 6009, WA, Australia
2National ICT Australia, Locked Bag 8001, Canberra 2601, ACT, Australia
3Computer Science Laboratory, Australian National University, Canberra 0200, ACT, Australia
Received 23 January 2005; Revised 29 May 2005; Accepted 22 August 2005
Copyright © 2006 Eric A. Lehmann and Robert C. Williamson. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Sequential Monte Carlo methods have been recently proposed to deal
with the problem of acoustic source localisation and tracking
using an array of microphones. Previous implementations make use
of the basic bootstrap particle filter, whereas a more general
approach involves the concept of importance sampling. In this
paper, we develop a new particle filter for acoustic source
localisation using importance sampling, and compare its tracking
ability with that of a bootstrap algorithm proposed previously in
the literature. Experimental results obtained with simulated
reverberant samples and real audio recordings demonstrate that the
new algorithm is more suitable for practical applications due to
its reinitialisation capabilities, despite showing a slightly
lower average tracking accuracy. A real-time implementation of the
algorithm also shows that the proposed particle filter can
reliably track a person talking in real reverberant rooms.